Apache SparkOpen-source multi-language engine for data engineering, data science and machine learning.
vs
DuckDBSimple, feature-rich, fast and open source analytical SQL database.
Comparison · Data Warehouse · weekly run Nov 3
Apache Spark vs DuckDB
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
DuckDB is the stronger AI pick in Data Warehouse: it has a visibility score of 9.9 versus 0.9 for Apache Spark, and ranks ahead in 100% of the answers that name both.
Choose Apache Spark if…
- You prefer a larger, established vendor (5,000+ employees)
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
Choose DuckDB if…
- You want the brand AI engines rank higher (9.9 vs 0.9)
- Open-source, in-process analytical database
- Free and excellent for local analytics on small datasets
- Lightweight, local-first data engineering without massive cloud infrastructure
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | DuckDB |
|---|---|---|
| Visibility score | 0.9% | 9.9% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #11 |
| Share of voice | 0.5% | 3% |
| Mentions this run | 3 | 17 |
| Positive in AI answers | — | 37% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · DuckDB 0pp over 0 weekly runs.
Engine by engine
DuckDB leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache SparkDuckDB
Head to head in AI answers
2 engine answers name both brands
0Apache Spark ranked higher
2DuckDB ranked higher
| Prompt | Engine | Apache Spark | DuckDB |
|---|---|---|---|
| How do companies store and query business data at scale without a dedicated tool? | ChatGPT | #9 | #4 |
| How do companies store and query business data at scale without a dedicated tool? | AI Mode | #11 | #10 |
Features, pricing & fit
| Dimension | Apache Spark | DuckDB |
|---|---|---|
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | Embedded in-process, or client-server via Quack, from edge devices to servers with hundreds of cores |
| Support | — | Support page on duckdb.org, GitHub issues |
| Founded | 2010 | 2018 |
| Headquarters | Wilmington, Delaware | Amsterdam, Netherlands |
| Employees | 5,000+ | 11–50 |
| Funding | — | Spinout from CWI |
| Best for | Data engineering, data science and machine learning work on single machines or clusters. | Developers and data engineers who want a fast analytical SQL engine embedded in an application or run on a laptop, edge device or server |
Pros and cons
Themes from the AI answers that mention each brand
Apache Spark
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
DuckDB
- Open-source, in-process analytical database
- Free and excellent for local analytics on small datasets
- Lightweight, local-first data engineering without massive cloud infrastructure
- Free tier and simple, predictable low-cost plans via MotherDuck
- Not suited to shared, high-concurrency application databases
- Less suited to large enterprise workloads
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs DuckDB”, https://slateindex.ai/compare/apache-spark-vs-duckdb, data of Nov 3, 2026. Licence: CC BY 4.0.